SUMMARY The primary goal of the proposed research is to demonstrate a high throughput flow cytometer system that can sort cells based on high-content 3D cell image features. For each single cell flowing in a microfluidic channel, the system will produce cell tomography from spatially resolved fluorescent and scattering signals at a rate of 1000 cells/s. Each multi-parameter 3D cell image will be reconstructed, hundreds of image features will be extracted, and cells with their spatial features meeting the user-defined criteria will be sorted (3D image-guided cell sorting). Essentially the proposed system combines the merits of high throughput cell analysis and sorting capabilities of a fluorescence-activated cell sorter (FACS) with a high-content 3D imaging microscope to offer researchers and clinicians unprecedented features and capabilities to analyze, classify, and isolate cells at single cell resolution. The invention of this tool is anticipated to transform cell phenotype studies, greatly accelerate cell type discoveries, and enhance studies of highly heterogeneous biological samples such as tumors and brain. To realize such ambitious goal, we will take several innovative approaches. To produce high-quality 3D cell images for individual cells travelling fast in a flow channel, we invent a camera-less imaging system using a design that combines scanning structured light excitation and the scheme of confocal detection, which transforms 3D spatial information into temporal signals at the output of high-speed photomultiplier tubes (PMTs). For cell sorting mechanism, we adopt a microfluidic chip/cartridge design with an on-chip piezoelectric actuator to sort cells without causing flow jitters that can disrupt imaging of cells passing the optical imaging area. To achieve real-time image processing and image feature extraction, as well as handling the transport and storage of the large amount of 3D cell image data, we propose an electronic system consisting of a field programmable gate array (FPGA) module and graphics processing unit (GPU), having the FPGA process PMT signals, cell detection, segmentation and image reconstruction, and sorting decision control while having the GPU extract hundreds of 3D image related features and define sorting criteria (i.e. 3D image- guided gating) in parallel. To evaluate the performance of the system, we will perform experiments to sort cells based on the properties of protein translocation and trafficking, spot counting, organelle tracking, and features that help understand the disease biology and drug development. The proposed instrument will offer biomedical community a powerful tool to advance phenotype studies and cell type discoveries, and to link gene expression studies to cell phenotypic characteristics at single cell resolution and high throughput. The impact of the research will be significant and profound.